Lithology identification is a very critical process in the process of geological exploration and development, and plays a very key role in the process of geological resource prediction and evaluation. With the development of the geological exploration industry, the traditional lithology identification is limited by time and space, and can no longer meet the increasing data scale and dimensionality. Therefore, there is an urgent need for in-depth research on the intelligent prediction of lithology identification, so as to promote the development of lithology identification in the direction of digitalization, intelligence and timeliness. In this paper, with the goal of realizing intelligent lithology identification and prediction based on drilling parameters, five algorithms are used to establish intelligent lithology identification and prediction models based on drilling parameters by using five algorithms: logistic regression, SVM support vector machine, K-nearest neighbor algorithm, random forest and neural network, respectively, to realize the intelligent prediction of drilling encounters, and the main research contents are as follows: A regional stratigraphic identification model based on drilling data was designed. Through the data preprocessing of the original data, and then the factor analysis of the formation parameters and other parameters, the parameters were reduced by factor analysis and then modeled by five machine algorithms, and an intelligent identification model of lithology was established with the drilling parameter matrix as the input, the lithology classification identification and formation parameter prediction as the output. Experiments show that most of the accuracy and F1 score of lithology identification on the test set under the five algorithms reach more than 70%, and the individual accuracy is low but also about 60%, and most of the indicators MAE and RMSE predicted by the stratigraphic parameters of the five models are below 1, and the identification and prediction are accurate.